



"""simple docstring"""





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	42
       UpperCamelCase		=	jnp.floataa
       UpperCamelCase		=	True



       def 	lowercase__							(				self     :    Any       )					->		Any:


             '''simple docstring'''




             super().setup()
             UpperCAmelCase_     					=				nn.Dense(5						,							dtype=self.dtype       )







       def __call__(				self     :    Optional[int]						,							*_UpperCAmelCase     :    List[str]						,							**_UpperCAmelCase     :    int       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				super().__call__(*_UpperCAmelCase						,							**_UpperCAmelCase       )
             UpperCAmelCase_     					=				self.cls(outputs[2]       )
             return outputs[:2] + (cls_out,)



class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	FlaxBigBirdForNaturalQuestionsModule



def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      def cross_entropy(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=None ):
            UpperCAmelCase_     					=				logits.shape[-1]
            UpperCAmelCase_     					=				(labels[..., None] == jnp.arange(lowerCAmelCase__ )[None]).astype("f4" )
            UpperCAmelCase_     					=				jax.nn.log_softmax(lowerCAmelCase__      ,     axis=-1 )
            UpperCAmelCase_     					=				-jnp.sum(labels * logits      ,     axis=-1 )
            if reduction is not None:
                  UpperCAmelCase_     					=				reduction(lowerCAmelCase__ )
            return loss

      UpperCAmelCase_     					=				partial(lowerCAmelCase__      ,     reduction=jnp.mean )
      UpperCAmelCase_     					=				cross_entropy(lowerCAmelCase__      ,     lowerCAmelCase__ )
      UpperCAmelCase_     					=				cross_entropy(lowerCAmelCase__      ,     lowerCAmelCase__ )
      UpperCAmelCase_     					=				cross_entropy(lowerCAmelCase__      ,     lowerCAmelCase__ )
      return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class  lowercase__  :







       '''simple docstring'''


       UpperCamelCase		=	"google/bigbird-roberta-base"
       UpperCamelCase		=	30_00
       UpperCamelCase		=	1_05_00

       UpperCamelCase		=	1_28
       UpperCamelCase		=	3

       UpperCamelCase		=	1
       UpperCamelCase		=	5

       # tx_args
       UpperCamelCase		=	3E-5
       UpperCamelCase		=	0.0
       UpperCamelCase		=	2_00_00
       UpperCamelCase		=	0.0_0_9_5

       UpperCamelCase		=	"bigbird-roberta-natural-questions"
       UpperCamelCase		=	"training-expt"
       UpperCamelCase		=	"data/nq-training.jsonl"
       UpperCamelCase		=	"data/nq-validation.jsonl"



       def 	lowercase__							(				self     :    Dict       )					->		List[str]:


             '''simple docstring'''




             os.makedirs(self.base_dir						,							exist_ok=_UpperCAmelCase       )
             UpperCAmelCase_     					=				os.path.join(self.base_dir						,							self.save_dir       )
             UpperCAmelCase_     					=				self.batch_size_per_device * jax.device_count()



@dataclass
class  lowercase__  :







       '''simple docstring'''


       UpperCamelCase		=	42
       UpperCamelCase		=	40_96  # no dynamic padding on TPUs



       def __call__(				self     :    str						,							_UpperCAmelCase     :    List[str]       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.collate_fn(_UpperCAmelCase       )
             UpperCAmelCase_     					=				jax.tree_util.tree_map(_UpperCAmelCase						,							_UpperCAmelCase       )
             return batch



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    Dict       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_					,       UpperCAmelCase_     					=				self.fetch_inputs(features["input_ids"]       )
             UpperCAmelCase_     					=				{
                 "input_ids": jnp.array(_UpperCAmelCase						,							dtype=jnp.intaa       ),
                 "attention_mask": jnp.array(_UpperCAmelCase						,							dtype=jnp.intaa       ),
                 "start_labels": jnp.array(features["start_token"]						,							dtype=jnp.intaa       ),
                 "end_labels": jnp.array(features["end_token"]						,							dtype=jnp.intaa       ),
                 "pooled_labels": jnp.array(features["category"]						,							dtype=jnp.intaa       ),
             }
             return batch



       def 	lowercase__							(				self     :    str						,							_UpperCAmelCase     :    list       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[self._fetch_inputs(_UpperCAmelCase       ) for ids in input_ids]
             return zip(*_UpperCAmelCase       )







       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    list       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				[1 for _ in range(len(_UpperCAmelCase       )       )]
             while len(_UpperCAmelCase       ) < self.max_length:
                   input_ids.append(self.pad_id       )
                   attention_mask.append(0       )
             return input_ids, attention_mask



def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__=None ):
      if seed is not None:
            UpperCAmelCase_     					=				dataset.shuffle(seed=lowerCAmelCase__ )
      for i in range(len(lowerCAmelCase__ ) // batch_size ):
            UpperCAmelCase_     					=				dataset[i * batch_size : (i + 1) * batch_size]
            yield dict(lowerCAmelCase__ )
@partial(jax.pmap      ,     axis_name="batch" )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     **lowerCAmelCase__ ):
      def loss_fn(lowerCAmelCase__ ):
            UpperCAmelCase_     					=				model_inputs.pop("start_labels" )
            UpperCAmelCase_     					=				model_inputs.pop("end_labels" )
            UpperCAmelCase_     					=				model_inputs.pop("pooled_labels" )

            UpperCAmelCase_     					=				state.apply_fn(**lowerCAmelCase__      ,     params=lowerCAmelCase__      ,     dropout_rng=lowerCAmelCase__      ,     train=lowerCAmelCase__ )
            UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				outputs

            return state.loss_fn(
                lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     )

      UpperCAmelCase_					,       UpperCAmelCase_     					=				jax.random.split(lowerCAmelCase__ )
      UpperCAmelCase_     					=				jax.value_and_grad(lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_     					=				grad_fn(state.params )
      UpperCAmelCase_     					=				jax.lax.pmean({"loss": loss}      ,     axis_name="batch" )
      UpperCAmelCase_     					=				jax.lax.pmean(lowerCAmelCase__      ,     "batch" )

      UpperCAmelCase_     					=				state.apply_gradients(grads=lowerCAmelCase__ )
      return state, metrics, new_drp_rng
@partial(jax.pmap      ,     axis_name="batch" )
def   a__      ( lowerCAmelCase__      ,     **lowerCAmelCase__ ):
      UpperCAmelCase_     					=				model_inputs.pop("start_labels" )
      UpperCAmelCase_     					=				model_inputs.pop("end_labels" )
      UpperCAmelCase_     					=				model_inputs.pop("pooled_labels" )

      UpperCAmelCase_     					=				state.apply_fn(**lowerCAmelCase__      ,     params=state.params      ,     train=lowerCAmelCase__ )
      UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				outputs

      UpperCAmelCase_     					=				state.loss_fn(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )
      UpperCAmelCase_     					=				jax.lax.pmean({"loss": loss}      ,     axis_name="batch" )
      return metrics
class  lowercase__  (       train_state.TrainState ):







       '''simple docstring'''


       UpperCamelCase		=	struct.field(pytree_node=SCREAMING_SNAKE_CASE )



@dataclass
class  lowercase__  :







       '''simple docstring'''


       UpperCamelCase		=	42
       UpperCamelCase		=	42
       UpperCamelCase		=	42
       UpperCamelCase		=	42
       UpperCamelCase		=	42
       UpperCamelCase		=	42
       UpperCamelCase		=	None



       def 	lowercase__							(				self     :    int						,							_UpperCAmelCase     :    int						,							_UpperCAmelCase     :    Dict						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    int=None       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				model.params
             UpperCAmelCase_     					=				TrainState.create(
                 apply_fn=model.__call__						,							params=_UpperCAmelCase						,							tx=_UpperCAmelCase						,							loss_fn=_UpperCAmelCase						,							)
             if ckpt_dir is not None:
                   UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				restore_checkpoint(_UpperCAmelCase						,							_UpperCAmelCase       )
                   UpperCAmelCase_     					=				{
                       "lr": args.lr,
                       "init_lr": args.init_lr,
                       "warmup_steps": args.warmup_steps,
                       "num_train_steps": num_train_steps,
                       "weight_decay": args.weight_decay,
                   }
                   UpperCAmelCase_					,       UpperCAmelCase_     					=				build_tx(**_UpperCAmelCase       )
                   UpperCAmelCase_     					=				train_state.TrainState(
                       step=_UpperCAmelCase						,							apply_fn=model.__call__						,							params=_UpperCAmelCase						,							tx=_UpperCAmelCase						,							opt_state=_UpperCAmelCase						,							)
                   UpperCAmelCase_     					=				args
                   UpperCAmelCase_     					=				data_collator
                   UpperCAmelCase_     					=				lr
                   UpperCAmelCase_     					=				params
             UpperCAmelCase_     					=				jax_utils.replicate(_UpperCAmelCase       )
             return state



       def 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[int]						,							_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    List[str]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.args
             UpperCAmelCase_     					=				len(_UpperCAmelCase       ) // args.batch_size

             UpperCAmelCase_     					=				jax.random.PRNGKey(0       )
             UpperCAmelCase_     					=				jax.random.split(_UpperCAmelCase						,							jax.device_count()       )
             for epoch in range(args.max_epochs       ):
                   UpperCAmelCase_     					=				jnp.array(0						,							dtype=jnp.floataa       )
                   UpperCAmelCase_     					=				get_batched_dataset(_UpperCAmelCase						,							args.batch_size						,							seed=_UpperCAmelCase       )
                   UpperCAmelCase_     					=				0
                   for batch in tqdm(_UpperCAmelCase						,							total=_UpperCAmelCase						,							desc=F"""Running EPOCH-{epoch}"""       ):
                         UpperCAmelCase_     					=				self.data_collator(_UpperCAmelCase       )
                         UpperCAmelCase_					,       UpperCAmelCase_					,       UpperCAmelCase_     					=				self.train_step_fn(_UpperCAmelCase						,							_UpperCAmelCase						,							**_UpperCAmelCase       )
                         running_loss += jax_utils.unreplicate(metrics["loss"]       )
                         i += 1
                         if i % args.logging_steps == 0:
                               UpperCAmelCase_     					=				jax_utils.unreplicate(state.step       )
                               UpperCAmelCase_     					=				running_loss.item() / i
                               UpperCAmelCase_     					=				self.scheduler_fn(state_step - 1       )

                               UpperCAmelCase_     					=				self.evaluate(_UpperCAmelCase						,							_UpperCAmelCase       )
                               UpperCAmelCase_     					=				{
                                   "step": state_step.item(),
                                   "eval_loss": eval_loss.item(),
                                   "tr_loss": tr_loss,
                                   "lr": lr.item(),
                               }
                               tqdm.write(str(_UpperCAmelCase       )       )
                               self.logger.log(_UpperCAmelCase						,							commit=_UpperCAmelCase       )

                         if i % args.save_steps == 0:
                               self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""						,							state=_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Tuple						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    Optional[int]       )					->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				get_batched_dataset(_UpperCAmelCase						,							self.args.batch_size       )
             UpperCAmelCase_     					=				len(_UpperCAmelCase       ) // self.args.batch_size
             UpperCAmelCase_     					=				jnp.array(0						,							dtype=jnp.floataa       )
             UpperCAmelCase_     					=				0
             for batch in tqdm(_UpperCAmelCase						,							total=_UpperCAmelCase						,							desc="Evaluating ... "       ):
                   UpperCAmelCase_     					=				self.data_collator(_UpperCAmelCase       )
                   UpperCAmelCase_     					=				self.val_step_fn(_UpperCAmelCase						,							**_UpperCAmelCase       )
                   running_loss += jax_utils.unreplicate(metrics["loss"]       )
                   i += 1
             return running_loss / i







       def 	lowercase__							(				self     :    Union[str, Any]						,							_UpperCAmelCase     :    Optional[Any]						,							_UpperCAmelCase     :    List[Any]       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				jax_utils.unreplicate(_UpperCAmelCase       )
             print(F"""SAVING CHECKPOINT IN {save_dir}"""						,							end=" ... "       )
             self.model_save_fn(_UpperCAmelCase						,							params=state.params       )
             with open(os.path.join(_UpperCAmelCase						,							"opt_state.msgpack"       )						,							"wb"       ) as f:
                   f.write(to_bytes(state.opt_state       )       )
             joblib.dump(self.args						,							os.path.join(_UpperCAmelCase						,							"args.joblib"       )       )
             joblib.dump(self.data_collator						,							os.path.join(_UpperCAmelCase						,							"data_collator.joblib"       )       )
             with open(os.path.join(_UpperCAmelCase						,							"training_state.json"       )						,							"w"       ) as f:
                   json.dump({"step": state.step.item()}						,							_UpperCAmelCase       )
             print("DONE"       )



def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      print(f"""RESTORING CHECKPOINT FROM {save_dir}"""      ,     end=" ... " )
      with open(os.path.join(lowerCAmelCase__      ,     "flax_model.msgpack" )      ,     "rb" ) as f:
            UpperCAmelCase_     					=				from_bytes(state.params      ,     f.read() )

      with open(os.path.join(lowerCAmelCase__      ,     "opt_state.msgpack" )      ,     "rb" ) as f:
            UpperCAmelCase_     					=				from_bytes(state.opt_state      ,     f.read() )

      UpperCAmelCase_     					=				joblib.load(os.path.join(lowerCAmelCase__      ,     "args.joblib" ) )
      UpperCAmelCase_     					=				joblib.load(os.path.join(lowerCAmelCase__      ,     "data_collator.joblib" ) )

      with open(os.path.join(lowerCAmelCase__      ,     "training_state.json" )      ,     "r" ) as f:
            UpperCAmelCase_     					=				json.load(lowerCAmelCase__ )
      UpperCAmelCase_     					=				training_state["step"]

      print("DONE" )
      return params, opt_state, step, args, data_collator
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				num_train_steps - warmup_steps
      UpperCAmelCase_     					=				optax.linear_schedule(init_value=lowerCAmelCase__      ,     end_value=lowerCAmelCase__      ,     transition_steps=lowerCAmelCase__ )
      UpperCAmelCase_     					=				optax.linear_schedule(init_value=lowerCAmelCase__      ,     end_value=1e-7      ,     transition_steps=lowerCAmelCase__ )
      UpperCAmelCase_     					=				optax.join_schedules(schedules=[warmup_fn, decay_fn]      ,     boundaries=[warmup_steps] )
      return lr


def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      def weight_decay_mask(lowerCAmelCase__ ):
            UpperCAmelCase_     					=				traverse_util.flatten_dict(lowerCAmelCase__ )
            UpperCAmelCase_     					=				{k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
            return traverse_util.unflatten_dict(lowerCAmelCase__ )

      UpperCAmelCase_     					=				scheduler_fn(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )

      UpperCAmelCase_     					=				optax.adamw(learning_rate=lowerCAmelCase__      ,     weight_decay=lowerCAmelCase__      ,     mask=lowerCAmelCase__ )
      return tx, lr






"""simple docstring"""






from __future__ import annotations



class 			lowercase					:






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	int = 0			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  key






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	list[str]:





				"""simple docstring"""


				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) and isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  key or self.__key or 1

				# make sure key is an appropriate size
				key %= 255

				return [chr(ord(SCREAMING_SNAKE_CASE_			) ^ key			) for ch in content]






			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	list[str]:





				"""simple docstring"""


				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) and isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  key or self.__key or 1

				# make sure key is an appropriate size
				key %= 255

				return [chr(ord(SCREAMING_SNAKE_CASE_			) ^ key			) for ch in content]






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int = 0			)       ->	str:





				"""simple docstring"""


				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) and isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  key or self.__key or 1

				# make sure key can be any size
				while key > 255:
					key -= 255

				# This will be returned
				lowerCAmelCase												=  ''''''

				for ch in content:
					ans += chr(ord(SCREAMING_SNAKE_CASE_			) ^ key			)

				return ans






			def 					UpperCAmelCase							(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int = 0			)       ->	str:





				"""simple docstring"""


				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) and isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  key or self.__key or 1

				# make sure key can be any size
				while key > 255:
					key -= 255

				# This will be returned
				lowerCAmelCase												=  ''''''

				for ch in content:
					ans += chr(ord(SCREAMING_SNAKE_CASE_			) ^ key			)

				return ans






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int = 0			)       ->	bool:





				"""simple docstring"""


				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) and isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				try:
					with open(SCREAMING_SNAKE_CASE_			) as fin, open('''encrypt.out'''      ,'''w+'''			) as fout:
						# actual encrypt-process
						for line in fin:
							fout.write(self.encrypt_string(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				except OSError:
					return False

				return True






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	str      ,SCREAMING_SNAKE_CASE_  :	int			)       ->	bool:





				"""simple docstring"""


				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) and isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)

				try:
					with open(SCREAMING_SNAKE_CASE_			) as fin, open('''decrypt.out'''      ,'''w+'''			) as fout:
						# actual encrypt-process
						for line in fin:
							fout.write(self.decrypt_string(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)			)

				except OSError:
					return False

				return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


